Intuitionistic fuzzy XML query matching and rewriting

With the emergence of XML as a standard for data representation, particularly on the web, the need for intelligent query languages that can operate on XML documents with structural heterogeneity has recently gained a lot of popularity. Traditional Information Retrieval and Database approaches have limitations when dealing with such scenarios. Therefore, fuzzy (flexible) approaches have become the predominant. In this thesis, we propose a new approach for approximate XML query matching and rewriting which aims at achieving soft matching of XML queries with XML data sources following different schemas. Unlike traditional querying approaches, which require exact matching, the proposed approach makes use of Intuitionistic Fuzzy Trees to achieve approximate (soft) query matching. Through this new approach, not only the exact answer of a query, but also approximate answers are retrieved. Furthermore, partial results can be obtained from multiple data sources and merged together to produce a single answer to a query. The proposed approach introduced a new tree similarity measure that considers the minimum and maximum degrees of similarity/inclusion of trees that are based on arc matching. New techniques for soft node and arc matching were presented for matching queries against data sources with highly varied structures. A prototype was developed to test the proposed ideas and it proved the ability to achieve approximate matching for pattern queries with a number of XML schemas and rewrite the original query so that it obtain results from the underlying data sources. This has been achieved through several novel algorithms which were tested and proved efficiency and low CPU/Memory cost even for big number of data sources

An IFTr approach to approximate XML query matching

As XML is becoming the de facto standard for data representation and communication, especially on the web, a need has been identified for efficient XML querying techniques that can overcome the dilemma of heterogeneity in XML data sources. In this work, we propose our approach of using Intuitionistic Fuzzy Trees (IFTr) to achieve approximate XML query matching, making the query results include not just the XML data trees that exactly match the query, but also the ones that partially match it. We believe that our approach has a potential to return useful query answers while pertaining good performance

Approximate XML query matching and rewriting using Intuitionistic Fuzzy Trees

XML is undoubtedly becoming the predominant de facto standard for data representation and communication, especially on the web, which in turn is causing XML data repositories to grow rapidly. Current XML Query languages, such as Xquery, have limited capabilities in querying multiple data sources with different structures (schemas) which is inefficient. Therefore, an urgent need has been identified for XML querying techniques that can overcome the rising diversity in XML data schemas. In this work, we propose our approach of using Intuitionistic Fuzzy Trees (IFTr) to achieve approximate XML query matching by considering a novel approach of matching arcs as basic units of data schemas. Additionally, we provide an algorithm for rewriting the original query to be able to retrieve data from local data sources. Our approach was tested using synthetic data sources with high degree of structural diversity, and it proved useful result while pertaining good performance and low memory usage

Arc-Based Soft XML Pattern Matching

The internet is undoubtedly the biggest data source ever with tons of data from different sources following different formats. One of the main challenges in computer science is how to make data sharing and exchange between these sources possible; or in other words, how to develop a system that can deal with all these differences in data representation and extract useful knowledge from there. And since XML is the de facto standard for representing data on the internet, XML query matching has gained so much popularity recently. In this paper we present new types of fuzzy arc matching that can match a pattern arc to a schema arc as long as the correspondent parent and child nodes are there and have reachability between them. Experimental results shown that the proposed approach provided better results than previous works

Enhancing DWH models with the utilisation of multiple hierarchical schemata

Data Warehouse (DWH) Models are based on static dimensions having single hierarchies. With the evolution of World Wide Web, including external knowledge can enrich those models and provide better results of data analysis. Therefore, it would be useful to have intelligent transformation utilities that can mine various data structures and extract useful knowledge participating in the construction of flexible DWH models. This paper proposes a new approach towards intelligent transformation utilities that will allow the utilisation of multiple hierarchical schemata when defining the dimensions of data-warehouses. By allowing a particular dimension to have multiple but semantically close definitions, we allow same users to query same data with the aid of different semantics. To put it differently users are allowed to change or refine the axis of analysis with respect to a particular query, in their effort to achieve a more meaningful answer. We make use of Intuitionistic Fuzzy Logic to soften the rules of calculating the similarity between different hierarchies which in turn is used to decide if the hierarchies can be included in the definition of data warehouse dimension. Data transformations are used to transform the data from one hierarchy to another. With the aid of external data, some sort of estimation is used to estimate values of new hierarchy levels and then based on the user's request the desired hierarchy is used to view an OLAP cube

Intuitionistic fuzzy XML query matching

As the popularity of XML as a de facto standard for data representation and communication is rising, a need has been identified for efficient XML querying techniques that can overcome the dilemma of diversity in the structure of XML data sources. In this work, we propose our approach of using Intuitionistic Fuzzy Trees (IFTr) to achieve approximate XML query matching, making the query result include not just the XML data trees that exactly match the query, but also the ones that partially match it. Our approach has a potential to return useful query answers while pertaining good performance. Users will have the option to choose between quick and less accurate results or time costing and more accurate results